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INDONESIA
Jurnal Sistem dan Manajemen Industri
Published by Universitas Serang Raya
ISSN : 25802887     EISSN : 25802895     DOI : -
Core Subject : Engineering,
Engineering Industrial & Manufacturing Engineering Transportation
This journal aims to publish the results of research in the field of Industrial Engineering is published twice a year, managed by the University of Serang Raya. The scope of Sciences covers Operations Research, Manufacturing System, Industrial Management, Ergonomics and Work System, Logistics and Supply Chain Management, and other scientific studies in accordance with scope field of Industrial Engineering research.
Arjuna Subject : -
Articles 12 Documents
 1   2 
Search results for , issue "Vol. 8 No. 1 (2024): June" : 12 Documents clear
Intelligent optimisation for multi-objectives flexible manufacturing cells formation Purnomo, Muhammad Ridwan Andi; Widodo, Imam Djati; Zukhri, Zainudin
Jurnal Sistem dan Manajemen Industri Vol. 8 No. 1 (2024): June
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v8i1.7974

Abstract

The primary objective of conventional manufacturing cell formation typically uses grouping efficiency and efficacy measurement to reduce voids and exceptional parts. This objective frequently leads to extreme solutions, such as the persistently significant workload disparity among the manu­facturing cells. It will have a detrimental psychological impact on operators who work in each formed manufacturing cell. The complexity of the problem increases when there is a requirement to finish all parts before the midday break, at which point the formed manufacturing cells can proceed with the following production batch after the break. This research examines the formation of manufacturing cells using two widely recognized intelligent optimization techniques: genetic algorithm (G.A.) and particle swarm optimisation (PSO). The discussed manufacturing system has flexible machines, allowing each part to have multiple production routing options. The optimisation process involved addressing four simultaneous objectives: enhancing the efficiency and efficacy of the manufacturing cells, minimizing the deviation of manufacturing cells working time with the allocated working hours, which is prior to the midday break, and ensuring a balanced workload for the formed manufacturing cells. The optimisation results demonstrate that the G.A. outperforms the PSO method and is capable of providing manufacturing cell formation solutions with an efficiency level of 0.86, efficacy level as high as 0.64, achieving a minimum lateness of only 24 minutes from the completion target before midday break and a maximum difference in workload as low as 49 minutes.
Optimizing business location for small and medium enterprises considering travel time uncertainty, natural disasters, and density population: a study case in Jakarta Sjahruddin, Herman; Dahlan, Ahmad Faisal
Jurnal Sistem dan Manajemen Industri Vol. 8 No. 1 (2024): June
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v8i1.8224

Abstract

This study addresses the critical problem of identifying optimal business locations for small and medium enterprises (SMEs), a decision-making process by factors such as travel time uncertainty, natural disasters, and population density. Existing research in this area has not adequately addressed these complexities, leaving a knowledge gap that this study aims to fill. Our research employs two optimization methods, differential evolu­tion (DE) and mixed integer programming (MIP), to maximize customer coverage. We present a comprehensive model that not only determines optimum and near-optimum business locations but also investigates the scalability of the algorithms with increasing facilities and their adaptability to different traffic scenarios. Key findings indicate that the DE algorithm, in particular, demonstrates superior coverage performance. This study contributes to the field by providing a robust and adaptable model for facility location problem-solving. The insights gained have practical applications for both academia and industry, aiding SMEs in making informed, strategic decisions about business location placement.

Page 2 of 2 | Total Record : 12

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